Using Artificial Neural Networks to Analyze Trends of a Global Aircraft Fleet

I’m an intern in the Commercial Mobility group at ViaSat.  Our group is responsible for all of the company’s commercial aviation clients, providing internet services to aircraft. While providing the world’s best in-flight internet service to airplanes traveling over 500 miles per hour 30,000 feet above the ground is no small feat, it is also a challenge to analyze and predict user demand of our network. There are typically several hundred planes connected to ViaSat’s network at any given time amounting to 15,000-40,000 flights a week depending on the season. With this much range and traffic, and flights leaving all times of day, all over the world, modeling anything about them becomes very difficult.

A Brief Recap of Machine Learning

Machine learning has recently risen as a popular buzz word across industry and academia and covers a wide range of fields and procedures using computers algorithms to process and meaningfully interpret real world data. What sets machine learning apart from traditional methods is, well, the learning. Instead of a developer programming every possible scenario, machine learning algorithms are trained on known data and left to interpret new data, based on what it has been taught.

What is an Artificial Neural Network?

Artificial neural networks (ANNs) are machine learning models inspired by synapses in the brain. They are constructed by connecting layers of nodes with a dense web of links, bordered by an input and output layer. The internal layers are referred to as hidden layers because anyone calling the model will not see or interact with them. They are hidden behind the interface of the input and output layers.

Mathematically we represent each node layer as a function and each link as a weight. The web of links between each layer can then be represented as a matrix. This matrix architecture has several advantages.

First, it allows the model to simultaneously process multiple sets inputs and return a set of outputs for each. Second, the entire network can be summed up in one simple equation:

However simple this equation is, it can still require massive amounts of processing to evaluate. ANNs can have anywhere from a few to several hundred layers with anywhere from a dozen to several thousand nodes.

Models with a high number of layers are called Deep Belief Networks and give rise to the term “Deep Learning.”

To evaluate these models we use video graphics cards. Surprised? You shouldn’t be. All the 3D graphics you see in video games use the same types of matrix math we harness in ANNs. Commercial gaming cards are built and optimized to compute huge matrixes very quickly, which makes them great for both gaming and running neural networks. If you’re more interested in just how exactly all this works, there are lots of resources out there. Lumiverse has a particularly good video series on the interworkings of ANNs.


As mentioned in the introduction above, the Commercial Mobility group at ViaSat provides the world’s best in-flight internet service to commercial aircraft.  With hundreds planes connected to ViaSat’s network at any given time around the world and with seasonal spikes in air travel, modeling the network becomes very difficult.

Fortunately, this kind of high-dimensional, non-linear problem is just what neural networks excel at. Using public data such as departure and arrival times, airline, and the origin and destination of flights, coupled with ViaSat’s internal metrics for flight usage we trained a neural network to predict the number of devices and the satellite link data use for all aircraft. These predictions are compared to the actual data at one hour intervals in the plots below.

Personal Electronic Device Count

Forward Link Data Use

Return Link Data Use

While these models are not perfect, they are substantially more accurate than the statistical models previously in use. The best part is, these models can continually be re-trained with more current data, keeping up with changing trends in user demand as our services grow and more airlines switch to ViaSat’s internet services. We can also always go back and add more input nodes, with more metrics to improve our understanding of when and why people use the internet while they fly.

Moving Forward

For those of us in Commercial Mobility, this is just the tip of the iceberg. Artificial neural networks provide us a whole new way to examine data and solve problems. We can use ANNs to identify common factors driving problems that could cause service outages and loss of flight connectivity. With this kind of insight, we can remedy edge-case scenarios that could otherwise cause failures, and we can take measures to prevent problems from reaching customers by predicting them far enough in advance. It’s a big complicated world out there and machine learning is giving us here at ViaSat a whole new perspective.

Editor’s Note:  This article was written by one of our interns describing his project for the summer.  ViaSat is proud to give our interns challenging, real, and valuable projects to work on during their internship. Keep an eye out for more posts from our amazing interns!  All posts will be tagged with the “Intern Project” label.

3 thoughts

  1. Zac,

    With a user base that large, identifying the crunch points becomes critical. Looks like the results of your modeling would be useful for determining network structure and how many resources to allocate. You may have already seen this video from space x but they discuss modeling in large sets:


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